Big Earth Data

📢 [Data Note] Geo-Disasters: Geocoding climate-related events in the international disaster database EM-DAT by Khalil Teber, Mélanie Weynants, Fabian Gans & Miguel D. Mahecha 👉 doi.org/10.1080/209644… 💌 #Climate #hazards can escalate into humanitarian #disasters. Understanding their trajectories—considering #hazard intensity, human exposure, and societal #vulnerability—is essential for effective anticipatory action. The International Disaster Database (EM-DAT) is the only freely available global resource of humanitarian disaster records. However, it has imprecise geocoded information, which severely constrains its integration with spatial climate and socioeconomic data, limiting its use for climate impact research and policy planning. Here, we present Geo-Disasters (doi.org/10.5281/zenodo…), a database that provides geocoded locations of 9,217 climate-related disasters reported by EM-DAT from 1990 to 2023, along with an open, reproducible framework for updating (doi.org/10.6084/m9.fig…). Our method remains accurate even when only region names are available and includes matching quality flags to assess reliability. The augmented EM-DAT enables integration with other geocoded data, supporting #machinelearning applications and cross-domain analyses of climate risks, vulnerabilities, and adaptation deficits. By making more extreme events available, Geo-Disasters aims to bridge critical data gaps in global climate-hazard #risk assessment and to inform more equitable adaptation planning. #EMDAT #climateextremes #resilience #geocoding #opendata #bigearthdata #digitalearth #geoscience #remotesensing #earthobservation #GIS #dataanalysis #BigData

📢 [Data Note] Geo-Disasters: Geocoding climate-related events in the international disaster database EM-DAT by Khalil Teber, Mélanie Weynants, Fabian Gans & Miguel D. Mahecha 👉 doi.org/10.1080/209644… 💌 #Climate #hazards can escalate into humanitarian #disasters. Understanding their trajectories—considering #hazard intensity, human exposure, and societal #vulnerability—is essential for effective anticipatory action. The International Disaster Database (EM-DAT) is the only freely available global resource of humanitarian disaster records. However, it has imprecise geocoded information, which severely constrains its integration with spatial climate and socioeconomic data, limiting its use for climate impact research and policy planning. Here, we present Geo-Disasters (doi.org/10.5281/zenodo…), a database that provides geocoded locations of 9,217 climate-related disasters reported by EM-DAT from 1990 to 2023, along with an open, reproducible framework for updating (doi.org/10.6084/m9.fig…). Our method remains accurate even when only region names are available and includes matching quality flags to assess reliability. The augmented EM-DAT enables integration with other geocoded data, supporting #machinelearning applications and cross-domain analyses of climate risks, vulnerabilities, and adaptation deficits. By making more extreme events available, Geo-Disasters aims to bridge critical data gaps in global climate-hazard #risk assessment and to inform more equitable adaptation planning. #EMDAT #climateextremes #resilience #geocoding #opendata #bigearthdata #digitalearth #geoscience #remotesensing #earthobservation #GIS #dataanalysis #BigData

📢 [Data Note] Geo-Disasters: Geocoding climate-related events in the international disaster database EM-DAT by Khalil Teber, Mélanie Weynants, Fabian Gans & Miguel D. Mahecha 👉 doi.org/10.1080/209644… 💌 #Climate #hazards can escalate into humanitarian #disasters. Understanding their trajectories—considering #hazard intensity, human exposure, and societal #vulnerability—is essential for effective anticipatory action. The International Disaster Database (EM-DAT) is the only freely available global resource of humanitarian disaster records. However, it has imprecise geocoded information, which severely constrains its integration with spatial climate and socioeconomic data, limiting its use for climate impact research and policy planning. Here, we present Geo-Disasters (doi.org/10.5281/zenodo…), a database that provides geocoded locations of 9,217 climate-related disasters reported by EM-DAT from 1990 to 2023, along with an open, reproducible framework for updating (doi.org/10.6084/m9.fig…). Our method remains accurate even when only region names are available and includes matching quality flags to assess reliability. The augmented EM-DAT enables integration with other geocoded data, supporting #machinelearning applications and cross-domain analyses of climate risks, vulnerabilities, and adaptation deficits. By making more extreme events available, Geo-Disasters aims to bridge critical data gaps in global climate-hazard #risk assessment and to inform more equitable adaptation planning. #EMDAT #climateextremes #resilience #geocoding #opendata #bigearthdata #digitalearth #geoscience #remotesensing #earthobservation #GIS #dataanalysis #BigData

📢 Assessing future risk of humanitarian crises using projections of climate-related hazards, population, conflict and other socioeconomic variables within the INFORM framework by Sepehr Marzi, Karmen Poljanšek, Eleni Papadimitriou, Daniele Dalla Valle, Andrea Salvi & Christina Corbane 👉 doi.org/10.1080/209644… 💌 This study applies the #INFORM Climate Change model to assess the global impacts of #climatechange on #humanitariancrises by integrating projections of #climate-related #hazards, #population, #conflict, #vulnerability, and coping capacity under different Shared Socioeconomic Pathways (#SSPs). Results suggest that global humanitarian #risk may decline under moderate and rapid development scenarios, whereas the high-emission SSP3 scenario could lead to over a 50% increase in populations living in high-risk countries due to rising #hazards and insufficient reductions in vulnerability and coping capacity. #bigearthdata #digitalearth #geoscience #remotesensing #earthobservation #GIS #dataanalysis #BigData #hazard #sustainabledevelopment #SDGs

📢 Assessing future risk of humanitarian crises using projections of climate-related hazards, population, conflict and other socioeconomic variables within the INFORM framework by Sepehr Marzi, Karmen Poljanšek, Eleni Papadimitriou, Daniele Dalla Valle, Andrea Salvi & Christina Corbane 👉 doi.org/10.1080/209644… 💌 This study applies the #INFORM Climate Change model to assess the global impacts of #climatechange on #humanitariancrises by integrating projections of #climate-related #hazards, #population, #conflict, #vulnerability, and coping capacity under different Shared Socioeconomic Pathways (#SSPs). Results suggest that global humanitarian #risk may decline under moderate and rapid development scenarios, whereas the high-emission SSP3 scenario could lead to over a 50% increase in populations living in high-risk countries due to rising #hazards and insufficient reductions in vulnerability and coping capacity. #bigearthdata #digitalearth #geoscience #remotesensing #earthobservation #GIS #dataanalysis #BigData #hazard #sustainabledevelopment #SDGs

📢 Assessing future risk of humanitarian crises using projections of climate-related hazards, population, conflict and other socioeconomic variables within the INFORM framework by Sepehr Marzi, Karmen Poljanšek, Eleni Papadimitriou, Daniele Dalla Valle, Andrea Salvi & Christina Corbane 👉 doi.org/10.1080/209644… 💌 This study applies the #INFORM Climate Change model to assess the global impacts of #climatechange on #humanitariancrises by integrating projections of #climate-related #hazards, #population, #conflict, #vulnerability, and coping capacity under different Shared Socioeconomic Pathways (#SSPs). Results suggest that global humanitarian #risk may decline under moderate and rapid development scenarios, whereas the high-emission SSP3 scenario could lead to over a 50% increase in populations living in high-risk countries due to rising #hazards and insufficient reductions in vulnerability and coping capacity. #bigearthdata #digitalearth #geoscience #remotesensing #earthobservation #GIS #dataanalysis #BigData #hazard #sustainabledevelopment #SDGs

📢 Evaluation of ten satellite-based and reanalysis precipitation datasets on a daily basis for Czechia (2001–2021) by Daniel Paluba, Vojtěch Bližňák, Miloslav Müller & Přemysl Štych 👉Article link: doi.org/10.1080/209644… 💌 This study evaluates the accuracy of ten #satellite-based and #reanalysis #precipitation datasets available in #GoogleEarthEngine (#GEE) using in-situ rain gauge observations across #Czechia from 2001 to 2021. Results show that the gauge-adjusted #GSMaPGA dataset performs best overall (r = 0.79), followed by #ERA5-Land (r = 0.75), while most datasets overestimate light rainfall and underestimate heavy rainfall; GSMaPGA is recommended for most applications due to its higher accuracy, whereas #ERA5-Land is suitable for long-term analyses given its extended historical record. #timeseries #Czechia #meteorology #geoscience #remotesensing #earthobservation #GIS #dataanalysis #BigData #machinelearning #geography #opendata

📢 Evaluation of ten satellite-based and reanalysis precipitation datasets on a daily basis for Czechia (2001–2021) by Daniel Paluba, Vojtěch Bližňák, Miloslav Müller & Přemysl Štych 👉Article link: doi.org/10.1080/209644… 💌 This study evaluates the accuracy of ten #satellite-based and #reanalysis #precipitation datasets available in #GoogleEarthEngine (#GEE) using in-situ rain gauge observations across #Czechia from 2001 to 2021. Results show that the gauge-adjusted #GSMaPGA dataset performs best overall (r = 0.79), followed by #ERA5-Land (r = 0.75), while most datasets overestimate light rainfall and underestimate heavy rainfall; GSMaPGA is recommended for most applications due to its higher accuracy, whereas #ERA5-Land is suitable for long-term analyses given its extended historical record. #timeseries #Czechia #meteorology #geoscience #remotesensing #earthobservation #GIS #dataanalysis #BigData #machinelearning #geography #opendata

📢 Evaluation of ten satellite-based and reanalysis precipitation datasets on a daily basis for Czechia (2001–2021) by Daniel Paluba, Vojtěch Bližňák, Miloslav Müller & Přemysl Štych 👉Article link: doi.org/10.1080/209644… 💌 This study evaluates the accuracy of ten #satellite-based and #reanalysis #precipitation datasets available in #GoogleEarthEngine (#GEE) using in-situ rain gauge observations across #Czechia from 2001 to 2021. Results show that the gauge-adjusted #GSMaPGA dataset performs best overall (r = 0.79), followed by #ERA5-Land (r = 0.75), while most datasets overestimate light rainfall and underestimate heavy rainfall; GSMaPGA is recommended for most applications due to its higher accuracy, whereas #ERA5-Land is suitable for long-term analyses given its extended historical record. #timeseries #Czechia #meteorology #geoscience #remotesensing #earthobservation #GIS #dataanalysis #BigData #machinelearning #geography #opendata

📢[Review Article] Geospatial data and workflows for environmental and sustainability compliance reporting: including the private sector by Thomas Blaschke, Anton Eitzinger, Yu Dong, Hamid Ebrahimy & Amin Naboureh 👉 doi.org/10.1080/209644… 💌 This article systematically examines how evolving #EU sustainability regulations (including #CSRD, #ESRS, EU Taxonomy, #SFDR and #EUDR) translate into requirements for geospatial data and #EarthObservation workflows in corporate reporting, with a particular focus on deforestation-related supply chains. By synthesising state-of-the-art EO approaches, the study proposes a conceptual framework of geospatial #workflows—risk screening, attribution and verification—while identifying key gaps between legal definitions and EO-derived data, and outlining a research agenda for interoperable, standardised and auditable geospatial infrastructures for compliance reporting. #remotesensing #SDG #governance #sustainability #GeoAI

📢[Review Article] Geospatial data and workflows for environmental and sustainability compliance reporting: including the private sector by Thomas Blaschke, Anton Eitzinger, Yu Dong, Hamid Ebrahimy & Amin Naboureh 👉 doi.org/10.1080/209644… 💌 This article systematically examines how evolving #EU sustainability regulations (including #CSRD, #ESRS, EU Taxonomy, #SFDR and #EUDR) translate into requirements for geospatial data and #EarthObservation workflows in corporate reporting, with a particular focus on deforestation-related supply chains. By synthesising state-of-the-art EO approaches, the study proposes a conceptual framework of geospatial #workflows—risk screening, attribution and verification—while identifying key gaps between legal definitions and EO-derived data, and outlining a research agenda for interoperable, standardised and auditable geospatial infrastructures for compliance reporting. #remotesensing #SDG #governance #sustainability #GeoAI

📢[Review Article] Geospatial data and workflows for environmental and sustainability compliance reporting: including the private sector by Thomas Blaschke, Anton Eitzinger, Yu Dong, Hamid Ebrahimy & Amin Naboureh 👉 doi.org/10.1080/209644… 💌 This article systematically examines how evolving #EU sustainability regulations (including #CSRD, #ESRS, EU Taxonomy, #SFDR and #EUDR) translate into requirements for geospatial data and #EarthObservation workflows in corporate reporting, with a particular focus on deforestation-related supply chains. By synthesising state-of-the-art EO approaches, the study proposes a conceptual framework of geospatial #workflows—risk screening, attribution and verification—while identifying key gaps between legal definitions and EO-derived data, and outlining a research agenda for interoperable, standardised and auditable geospatial infrastructures for compliance reporting. #remotesensing #SDG #governance #sustainability #GeoAI

📢 An introduction to the OGC/ISO coverage and datacube standard for modeling multi-dimensional, spatio-temporal Big Data by Peter Baumann @pefrabau 👉doi.org/10.1080/209644… 💌 With #Earth data increasingly forming multidimensional spatio-temporal datacubes, coverages provide standardized data structures for representing space- and time-varying phenomena through a mature #ISO–#OGC standard framework centered on the Coverage Implementation Schema (#CIS). This paper introduces and explains ISO/OGC CIS (ISO 19123-2), its concepts, evolution, related standards, and example implementations, serving as a companion paper to support understanding and practical adoption of the #standard. #datacube #rasdaman #bigearthdata #digitalearth #geoscience #remotesensing #BigData #eLearning #earthsystem #INSPIRE

📢 An introduction to the OGC/ISO coverage and datacube standard for modeling multi-dimensional, spatio-temporal Big Data by Peter Baumann @pefrabau 👉doi.org/10.1080/209644… 💌 With #Earth data increasingly forming multidimensional spatio-temporal datacubes, coverages provide standardized data structures for representing space- and time-varying phenomena through a mature #ISO–#OGC standard framework centered on the Coverage Implementation Schema (#CIS). This paper introduces and explains ISO/OGC CIS (ISO 19123-2), its concepts, evolution, related standards, and example implementations, serving as a companion paper to support understanding and practical adoption of the #standard. #datacube #rasdaman #bigearthdata #digitalearth #geoscience #remotesensing #BigData #eLearning #earthsystem #INSPIRE

📢 An introduction to the OGC/ISO coverage and datacube standard for modeling multi-dimensional, spatio-temporal Big Data by Peter Baumann @pefrabau 👉doi.org/10.1080/209644… 💌 With #Earth data increasingly forming multidimensional spatio-temporal datacubes, coverages provide standardized data structures for representing space- and time-varying phenomena through a mature #ISO–#OGC standard framework centered on the Coverage Implementation Schema (#CIS). This paper introduces and explains ISO/OGC CIS (ISO 19123-2), its concepts, evolution, related standards, and example implementations, serving as a companion paper to support understanding and practical adoption of the #standard. #datacube #rasdaman #bigearthdata #digitalearth #geoscience #remotesensing #BigData #eLearning #earthsystem #INSPIRE